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In vitro molecular machine learning algorithm via symmetric internal loops of DNA

Title
In vitro molecular machine learning algorithm via symmetric internal loops of DNA
Author
이승환
Keywords
Biomolecular computation; Hypernetwork; Machine learning; Classification
Issue Date
2017-08
Publisher
ELSEVIER SCI LTD
Citation
BIOSYSTEMS, v. 158, page. 1-9
Abstract
Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules. (C) 2017 Published by Elsevier Ireland Ltd.
URI
https://www.sciencedirect.com/science/article/pii/S0303264717300357?via%3Dihubhttp://repository.hanyang.ac.kr/handle/20.500.11754/115355
ISSN
0303-2647; 1872-8324
DOI
10.1016/j.biosystems.2017.04.005
Appears in Collections:
GRADUATE SCHOOL[S](대학원) > BIONANOTECHNOLOGY(바이오나노학과) > Articles
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